{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(3.4922)\n",
      "500 tensor(0.0508)\n",
      "1000 tensor(0.0137)\n",
      "1500 tensor(0.0037)\n",
      "2000 tensor(0.0010)\n",
      "2500 tensor(0.0003)\n",
      "3000 tensor(7.0988e-05)\n",
      "3500 tensor(1.9060e-05)\n",
      "4000 tensor(5.1192e-06)\n",
      "4500 tensor(1.3754e-06)\n",
      "5000 tensor(3.6986e-07)\n",
      "5500 tensor(1.0027e-07)\n",
      "6000 tensor(2.7859e-08)\n",
      "6500 tensor(7.6585e-09)\n",
      "7000 tensor(2.4561e-09)\n",
      "7500 tensor(5.7470e-10)\n",
      "8000 tensor(5.3633e-10)\n",
      "8500 tensor(5.3633e-10)\n",
      "9000 tensor(5.3633e-10)\n",
      "9500 tensor(5.3633e-10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w tensor([2.9999], requires_grad=True)\n",
      "b tensor([0.8000], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "#1. 准备数据 y = 3x+0.8，准备参数\n",
    "x = torch.rand([50])\n",
    "y = 3*x + 0.8\n",
    "\n",
    "w = torch.rand(1,requires_grad=True)\n",
    "b = torch.rand(1,requires_grad=True)\n",
    "\n",
    "def loss_fn(y,y_predict):\n",
    "    loss = (y_predict-y).pow(2).mean()\n",
    "    for i in [w,b]:\n",
    "\t\t#每次反向传播前把梯度置为0\n",
    "        if i.grad is not None:\n",
    "            i.grad.data.zero_()\n",
    "    # [i.grad.data.zero_() for i in [w,b] if i.grad is not None]\n",
    "    loss.backward()\n",
    "    return loss.data\n",
    "\n",
    "def optimize(learning_rate):\n",
    "    # print(w.grad.data,w.data,b.data)\n",
    "    w.data -= learning_rate* w.grad.data\n",
    "    b.data -= learning_rate* b.grad.data\n",
    "\n",
    "for i in range(10000):\n",
    "    #2. 计算预测值\n",
    "    y_predict = x*w + b\n",
    "\t\n",
    "    #3.计算损失，把参数的梯度置为0，进行反向传播 \n",
    "    loss = loss_fn(y,y_predict)\n",
    "    \n",
    "    if i%500 == 0:\n",
    "        print(i,loss)\n",
    "    #4. 更新参数w和b\n",
    "    optimize(0.01)\n",
    "\n",
    "# 绘制图形，观察训练结束的预测值和真实值\n",
    "predict =  x*w + b  #使用训练后的w和b计算预测值\n",
    "\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rc('font', size=20)\n",
    "plt.rc('figure', figsize=(20, 8), dpi=100)\n",
    "plt.scatter(x.data.numpy(), y.data.numpy(),c = \"r\")\n",
    "plt.plot(x.data.numpy(), predict.data.numpy())\n",
    "plt.show()\n",
    "\n",
    "print(\"w\",w)\n",
    "print(\"b\",b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 准备数据\n",
    "x = torch.rand([500, 1])\n",
    "y = 3 * x + 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.9869], requires_grad=True)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = torch.rand(1, requires_grad=True)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.2133], requires_grad=True)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = torch.rand(1, requires_grad=True)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10000):\n",
    "    # 2. 进行预测\n",
    "    y_pre = w * x + b\n",
    "    \n",
    "    # 3. 计算损失\n",
    "    loss = (y_pre - y).pow(2).mean()\n",
    "    \n",
    "    # 4. 反向传播\n",
    "    if w.grad:\n",
    "        w.grad.data.zero_()\n",
    "    if b.grad:\n",
    "        b.grad.data.zero_()\n",
    "    loss.backward()\n",
    "    \n",
    "    # 5. 更新参数\n",
    "    w.data -= 0.01 * w.grad.data\n",
    "    b.data -= 0.01 * b.grad.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2.9999], requires_grad=True)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.8000], requires_grad=True)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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VwnSJiHSntszuqnB32ikf5wsOVnrHR+RueY88DRsTlgAAUEoQmgAAAAAAgDItZMwohcyeLovPl9NmSDknTH46t45adZ0WcP4r7z2n5n98ftJnZF/pldHqXqV37SFP45sISgAAKIUITQAAAAAAQJmVfQXXiXwWqzq0e05fXHCF6byr//tVS98eJrvPm6/n+KKi5XxuAsXdAQAo5QhNAAAAAABA2eD1yr5tq6z79spXvYY811ynkNnTJR0/VbKuznXq1uaZgEusWPSYrk78PWB/9okS1xNPytOoiawH9mc9iyu4AAAoEwhNAAAAAABAqeeIj1PYyKGyJSTktHkjImTx+eSyV1TTXvN0ICzSdO6DP36iiR/4F4Q344uuKee48ZwoAQCgjCI0AQAAAAAApY/XK/umDQpatljWv/6Uffs3fkPeqnuTnr6zb8AlNr3SXecf2XfqR1WpqowH2srdoiUnSgAAKOMITQAAAAAAQMmX6+ot286/FDxzmqypTr9h+0Mr64b+bwRcZsiG19Vv27KTPir7Cq60Xn0JSgAAKGcITQAAAAAAQInmiFupsGGDZUtKymkzThgz7tbuevWG+8znZ3r01YxOOicjNU979honXsvlqxwp5+RpXMEFAEA5RGgCAAAAAABKJrdbEQ/dJ8eWTX7BhkXSb1Uv1F3dZwacPjVugu79ZaMk/5Alp6B7nwHy3H6n7Fs2SobkaRIjT5MYTpYAAFBOEZoAAAAAAIASJ2TMKIXMmiaLkTfu8Mmibg+O1vq615nOq7d/l1YtGqyK3sy8HVar5PPl+r5Nrj795Ro9VpLkiWlapPsHAAClE6EJAAAAAAAoUULGjFLIzKl52jZfeKU6tXsu4Jylbw3TDf/+7NduWCzyRUXr0OfbFbzoNVl375KvVm2lde0pORxFvncAAFC6EZoAAAAAAICSw+1WyOzpkqSMCg7d0X2W/qlUw3To3b9u1sxVL/pd3ZXNsGT1OMeNl4KDlfZov+LYMQAAKEMITQAAAAAAQIkRvGCell3WTEPv/l/AMevm9lKd5IRTruWLipZz3HgKugMAgHwjNAEAAAAAAMXP7VbwgnkBr8dKdrl15+xtkq6Q7r7Cb3r/rUv0xKY3T/qI7OonGXc0V3rfAfI0bExBdwAAUCCEJgAAAAAAoFiFjBmlkNnTZclViD109Ai5+gzQ5Nu66bUv/gk495tpHVQl7ahfuyH5X8tlscrVd0BOcXcAAICCIjQBAAAAAADFxqyo+87K0WrWa27Wd0wCk/EfTlPbHz4OWKskW9o9rWUJqiilpirzxkZK6/4oxd0BAEChEJoAAAAAAIDC83pl37ZV1n175ateI+tqLK83p6i7JPW990l9UO8m0+k1zwnSkkeuVZDdphDnJ9IPH5ufJpHkCwpWyow5cse2LpaPAgAAyi9CEwAAAAAAUHDZIUliguwb16vimtWyHj58vDs6Wu6bb9VX0fXVtuP4gMu8uWSErurRTmk9+uW0ZV+vFTJ7upTrSi/DYlH6vffJOXs+tUoAAECxIDQBAAAAAAAF4oiPU9jIobIlJJj2u60VdM9dw/XbubWlC9v79Tf780u9unysrMdKt7t23+g3xjV6rFzDR520eDwAAEBRIzQBAAAAAACnduxkiWPNagXPmWU6JK7+zRoYOzTgEh/N76dLDu7xa/fVqm0+weFQ2qP9zPsAAACKAaEJAAAAAAAIzOtV8JSJCpk7K8/1W9mOVAzVdQPelMdmN53e7auVGrXuVdPaJIYkWW1ZJ0gAAABKAEITAAAAAABwXK6C7radfyl43iuyJh/yGzazYRtNbPpIwGW+nNFZ56YmS8oKR04s6m4c+9bVpz9XbgEAgBKD0AQAAAAAAEher0JemqDg2dNldTpzmo1cQ/45p7pies8PuMSzH8/Ww9+u9mtPb/2AguJW5CnqLqtNrj79c4q+AwAAlASEJgAAAAAAlHOO+DiFD+gta6rTtP/xu/+n5VfcbtpXzXlI6+f2Uqgn3a/PkOSLrinn7FflnDGHou4AAKDEIzQBAAAAAKC8yXUFl3XnXwqd8LzfkO+iLlbrh18KuMRry55Rs51fB+zPPqHiHDdestkkm42i7gAAoMQjNAEAAAAAoBxxxMcpbORQ2RISctqy641kWqxq23G8ttesbzr3xr9/1FtLRqiC4TPtz81XOVLOydPkbhVbRDsHAAAofoQmAAAAAACUVblOlPiq15AlKUkRPR+RDCPPsI8vaqhH7x8ZcJn4hYN0+b6/8vVIX+VIpfXsLdfgIVknTAAAAEoRQhMAAAAAAMogR9xKhQ97TNakgzlthtUqGYYsklIcwWrSZ4GOBoWZzu/w7Yd67uOZsgRYPzt2SevRW74LL5SvSlX5oqLladiYsAQAAJRahCYAAAAAAJQVx06WBM+aJscnH/kFHhafT69dG6tnb+8VcIkts7qqZsqBUz6K67cAAEBZRGgCAAAAAEBp5/UqeMpEhcydJevhw37dieFV1Kjv6wGnj1g3Xz2/WmHal13vJBvXbwEAgLKM0AQAAAAAgNLG65V9yybZt2yS7Y/f5fhsraypTr9hT9/eW4uubWW6RFiGS1tndVGE23XKx6W1fkCe5nfLV70G128BAIAyjdAEAAAAAIDSwu1W2GMDVHHFu7J6PKZDdlSrrbu7TQ+4xOwVz6vF71v92k88USJJ3rBwOV+eKXds69PfMwAAQClCaAIAAAAAQEnn9Sq8T3dVXPmeaWF2r8Wqzm3HamutK02nX5nwm959a6jsPq9pv2HJWjX1iSclb6ZkSJ4mMfI0ieFUCQAAKFcITQAAAAAAKKmya5VMnSxrRoZf9/ra16hL22cDTl/+xhO6NuFXv3bDapXF58v5vi8qWs5x4ynqDgAAyj1CEwAAAAAASiBHfJzCHx8oa/KhPO0ue0U16zlHe8Orms6776d1emn1S6YnUoxj3x595TUZ1arJum8vdUoAAAByITQBAAAAAOBs83pl37Y1J8SwJCUpoucjkmHkDHnryuYa0bx/wCU2zOmhCw/vPeWjXP0Gyd36/iLZNgAAQFlDaAIAAAAAwFnkiI9T2MihsiUk5LQZVqtkGDoYUknXD3gz4NzHNr2pgVuX5Os53ipV5Rz/EkXdAQAAToLQBAAAAACAM+3YyRLHh6sVPHeWX/eLNz+iOTc+YDrV6vNq+/SOqpTuPPVjwsKV0aGz3C1acgUXAABAPhCaAAAAAABwBpmdLJGkP6qcrzt6zA447+X3J6n1jvX5eoavcqTSevaWa/AQghIAAIACIDQBAAAAAKA4eL2yb9kk++aNsv33j7w1z5dRoYJCJ72YM8Qni3rdP0KfXtTQdImLDu5R/MJBqujNPOXjfMHBSn+4q9zNOVUCAABwughNAAAAAAAoSl6vQl6aoODpU2RNT/frNiR9fsEV6tD+hYBLLHn7STX856d8Pc5nt8s16AmlPT6UoAQAAKCQCE0AAAAAACistDSFjX5K9vVrZf37b1l9Pr8h6Ta7WnSboV2RNU2XaP7bFs1e+YIs+Xicr4JdmdfdINdjQ+SJaUpYAgAAUEQITQAAAAAAOB1ut4IXzFPQ3Fdk+2dPwLBjxaW3aPA9TwRc5tN5vfV/h/71azckyWqVJVcAQ60SAACA4kVoAgAAAABAAYWMGaWQ2dPzBBq5OR3BGnL3//ThJU1M+3tvW6YnN7wecH3DkhXBHJ27UEaVKrLu2ytf9RrUKgEAAChmhCYAAAAAABRAyJhRCpk51bRv9SVN1K/18IBzv5reSdVch0/5DF9UtJzjxsvdKvZ0twkAAIDTQGgCAAAAAEAgXq/s27YeP+lxzXUKmT1dknKu49ofWkm97xuh7TXrmy7x1pIRarLn+5M+xrBYJMNQ2qN95W7ekhMlAAAAZwmhCQAAAAAAuXm9sm/coJDJE1Rh+1eyejw5Xb6ICFl8PhmS5l8Xq7G39TJdotP21Rq1bp4qejNN+w0pTw0UTpYAAACUDIQmAAAAAABke+89RT78iKypTtPunRXC9UjvKfrvnOp+fZVdR/TWkpG69MCukz7COPZt6tCn5K1Tl1olAAAAJQihCQAAAAAAkhzxcVLXTnlOgEhSpsWqiU0f0ZwbHzCd98TGRer7+TJZc+KQk/NVjpRz8jROlQAAAJRAhCYAAAAAAHi9Ch0+RNLxa7O+ia6nhzq8qEyb/1+d6+3fpVeXP6vzjh7I1/I+u13uO1sovWsPeZrEcKoEAACghCI0AQAAAACUe/ZtW2XbmyiXvaKeuqu/Vl52q+m4CR+8rLY/fipJpzxXYthsymjRSulduhOUAAAAlBKEJgAAAACAcm/DzmQNGhZv2td059ea+v4kVUo/oc6J1Sr5fDnfNST5qlWT99LL5eozQJ6mtxKUAAAAlDKEJgAAAACAcinZ5dbw+F/0zT9HJFXy61+wbLRu3fmN6Vxvlao6tP1nBS96Tdbdu+SrVVtpXXtKDkfxbhoAAADFitAEAAAAAFB2eL2yb9kk+5ZNkkXyNLlZnsY35Zz4MAxD7/2QqBc//dN0+kPff6Qxn85RUKbbtD/7Si7n+Jek4GClPdqvOD4FAAAAzhJCEwAAAABAmeCIj1PY4wNkS04+3vjSRPkqR2rHCzPU93B1/Z2c5jcvxG7TrLYNdM03nyli/PRT1ipx9Rskd2zrIt07AAAASgZCEwAAAABA6eP1yr5tq6z79spXvYYsSUmK6PFw3iEWq16+qYOmN24n7ZKkvIFJ94YXqGejC2WzWiRJ7lax0vLlMh5+RJbUE+qXSDKCg3V0+hwCEwAAgDKM0AQAAAAAUDq43QpeME/29etk/+oLWY8ezekyLFnBh0XSDzX+T+3avyCXI9hviQsrB+vl+y/XeZX8+yRJ99+vQ02aqcLGDaq49C3Z/v5HvvMvUPpDHeSJaUphdwAAgDKO0AQAAAAAUOKFjBmlkNnTZfH5TPszbHY9fUdvLW1wp2n/uI9mquN3H+rIitXyBApMstls8tzSTJ5bmhV22wAAAChlCE0AAAAAACXTsSu4gmdNk+OTj0yHfFbnWnVtM8a0r9Ge7zVj1XhVSTt+IsW6b2+xbBUAAABlA6EJAAAAAKDEccTHKWzkUNkSEvz6DgeFaeA9Q7SxzrWmc+cuH6s7//zCtM9XvUaR7hMAAABlC6EJAAAAAODsy1XY3brzL4VOeN5vyNIrbtfQu/9nOr31z5/p+Y9mKMSTYdpvSPJF15SnYeMi3DQAAADKGkITAAAAAMDZ4fXKvmWTgl6fL8dn62R1puR0Gcoq6v5vRDX1eOBp/Xpubb/pdq9HS94ermsTfj3pY4xj3zrHjaeQOwAAAE6qUKHJhg0btHz5cn333Xc6dOiQHA6HLrzwQjVt2lQPP/ywIiMjC7zmvHnzNGnSpFOO69atm4YNG3Y62wYAAAAAnA3Zp0kSE2TfuF4V41bI6nL5DfPJopmN2mryzZ1Nl3l027sasnGRKhh5i8IbkmSxyGIYedsjI5UyaZrcrWKL6pMAAACgjDqt0CQzM1NPPvmk3n///TztHo9HO3bs0I4dO7R06VLNnDlTV199dYHW3rFjx+lsCQAAAABQgjniVip82GOyJh0MOGZHtdrq2G6ckkPO8eureWSfFi19WnUP/Wc6NzswOTrvdRmVKsm+ZZNkkTxNbpan8U2cMAEAAEC+nFZoMnny5JzA5LbbblOPHj1Uu3Zt7d+/Xxs2bNCsWbOUlJSk3r17Ky4uTtWrV8/32tmhSe/evdWrV6+A4+x2++lsHQAAAABwhoWMGaWQmVNlMenLsFXQ2GY99eY1LU3njv50jrp8877p3Nx80TXlHDc+5zSJ5+ZbCrVnAAAAlE8FDk327dunRYsWSZLuueeePFdpVa5cWZdccokaNmyo9u3b6/Dhw5ozZ46efvrpfK3tdDq1Z88eSdI111yj0NDQgm4PAAAAAFCCOOJWKmTmVL/2LRdeqY7tnjOdc81/v+iVFc/r3NTkk65tWK1y33aH0voOzCrwzmkSAAAAFFKBQ5NPP/1UmZmZkqTBgwebjmnQoIFuvfVWffLJJ1q/fn2+Q5MdO3bIOHb37OWXX17QrQEAAAAAShKvV2HDBuecEjnqCNHjLQfrk4sbmQ6fsepFtfp1c8DlsiuVuG+7Q55bmimta0/J4SjaPQMAAKBcK3Bosn//fgUFBSksLEw1a9YMOO6CCy7IGZ9f2Vdz1axZU1WqVCno1gAAAAAAJYh921bZkpK0qn5TDYodYjrm7l83a8KHUxXmTvPrM6Q813KdeAUXAAAAUNQKHJoMHjxYgwcPltPpPOm47Gu2zjnHv4BfID/99JOkrFMmH3zwgZYvX64ff/xRLpdLNWrU0M0336wePXooOjq6oNsGAAAAABQlt1vBC+bJunuXfLVq+5362J+SoaHb3fp5WLzp9CVvP6mG//wUcPnsUyWpQ5+St05d+arX4AouAAAAFLvTKgQvSWFhYQH7EhMTtWHDBklZtUnyK/ukybp16/TRRx/l6fvnn3/01ltv6b333tPkyZN12223ncauAQAAAACFFTJmlEJmT5fF58tpCx09Qql9Bmju3b00beOuY632PPO6fr1Kwz9bIIcv85TP8FWOlHPyNE6VAAAA4Iw67dAkEJ/Pp1GjRsnj8UiSOnTokK95LpdLu3Zl/cHa4/GoZcuW6tSpk2rVqqUjR47ok08+0ezZs+VyuTRo0CC99dZbuvLKKwOuZ7EE7Cp3sn8s+DEBUFx4zwAobrxngJIj5JlRCj6hsPsfVc5Xx4fGaX9YFSknMMlSzXVEbyx+SvUO7snX+r7QMKX1H6i0wUMkm01n8rc97xoAxY33DIDixnum8CxGduX1IjJu3Di98cYbkqSWLVvqpZdeyte8v/76S7169dLevXs1cOBAPfroo35jvvvuO3Xq1Ekej0eXX365li9fbrqW1+uTzWY9/Q8BAAAAAPDndkshIZLXK4/Vphdu6arXrm9tOvTJFvX06M11ZFmxQnrwQelkf/WMiJDuvFPq3Vu65Rau4AIAAMBZU2ShiWEYev7557Vo0SJJ0sUXX6wlS5YoNDS0QOt4vV7ZTvIH5LFjx+rNN9+UJK1atUr16tXzG3PgQApJWi4Wi1SlSriSklJO+vcUADhdvGcAFDfeM0DJEPTKTO2Y86badhxv2n9F4h+au2Kcwoc+ofTe/XLaHfFxCh0xVLaEhJw2b0SEMh7qIE/Le0pMrRLeNQCKG+8ZAMWN90xgVauG52tckVzP5Xa7NXz4cMXHZxX4q1u3rl577bUCByaSThqYSNJtt92WE5r88MMPpqGJdPJ/xFReGQY/LgCKF+8ZAMWN9wxQDLxe2bduln3LRsmQPE1i5GkSkyfESHVnatxHv+tT5xWSSWDyUvxk3f/zZznfd+3elef3akbLWGU0byn7tq2y7ttrXtS9BP3e5l0DoLjxngFQ3HjPnL5ChyaHDh1Sv379tH37dknSZZddpldffVWRkZGF3pyZqKioPM8GAAAAAJweR9xKhf+vn6zOlOONUybKW7mynJOna83FjTTs/V9M597+xxeavPolnZOR6tfnq1Xbf4LNlhXGAAAAACVYoUKT3bt3q1evXtqzJ6ugX0xMjKZOnXpaJ0yyGYYhy0nu1souMC9JwcHBp/0cAAAAACjPQsaMUsjMqX6F1g+EVFKfu5/U179Vkn7zD0wWLX1aMbu2mxZoNyTJalNa155Fv2EAAADgDDjt0OTXX39Vly5dlJycLElq27atRo8erQoVTm/JCRMmaNWqVUpLS9O2bdvkcDhMx/355585X9eqVeu0ngUAAAAA5Ub29Vub1sv677/ynXeeDFsFhcycmjPEkLTw2ns05vZHTZe4v0GUHr+1rhwVrApxxkgzt8uQ8gQn2bc/uPr0lwL8fQ4AAAAo6U4r4dizZ4+6deuWE5gMGjRIffv2LdRGKlWqpIMHD0qSvvzyS910002m495//31JUkhIiK699tpCPRMAAAAAyjJHfJzCHx8oa7L51cY7K0frkbbP6p9KNfz6zklL0VtLRui8OdPlaXJRTrtr9FhJUsjs6ZLPd3yC1SZXn/45/QAAAEBpVODQxO12a/DgwUpKSpIkDR8+XF26dCn0Ru6++25NnTpVmZmZGj9+vG644Qa/0ybx8fFat26dJKldu3YKCwsr9HMBAAAAoEzwemXfskn2LZskwydrYqKC3nnbb1imxapJN3fWKw3bmC7z2KY31X/rO7IeOztydN9evzGu0WPlGj5KwQvmybp7l3y1amddycUJEwAAAJRyBQ5Nli5dqp9//lmS1Lx5c7Vp00apqf6F/3LLXeOkefPmkqQGDRpowoQJOe3nnXeeunXrprlz5+r333/XQw89pMGDB+vSSy/VkSNHtGLFCi1YsECSVLduXQ0YMKCgWwcAAACAMskRH6ewxwfIduw2ADPboy9Ru/Yvyl3B7td38YE9mv/uGJ1/dL9fn6+6/ymUrIc6lPZov9PeMwAAAFASFTg0WbRoUc7Xa9as0Zo1a04557fffsv5eteuXZKkatWq+Y0bPHiwjhw5onfeeUc7duxQz57+xQPr16+vOXPmKCQkpKBbBwAAAIAyxxEfp4hunUz70ipU1Ii7+um9y5uZ9o//cKoe+uET0z5Dki8qWp6GjYtqqwAAAECJV6DQJDk5WXv27CmuvchqterZZ59VixYttHjxYn377bdKTk5WaGioLrroIrVs2VJt2rQ57WLzAAAAAFDqud3Hr8W6oJaCZ2UVdM9dlP3Tujeox4NPm06P2bVd0+ImqnJ6SsBHZBd1dz43QbLZimjjAAAAQMlXoPShcuXKeU6NnI78zG/UqJEaNWpUqOcAAAAAQJni9SqsTw8FrXpPFsPw6z4UHKH+scO0tdaVptNfW/aMmu38Ol+P8oWGyTn9FblbxRZqywAAAEBpw5ENAAAAACiJvF7Zt22Vdd9e2Xb+peCZ02RNdeYZYkhafOVdeqq5ec3HB3/8RGM/fkXBmRmm/YbynlDxhYUrrXc/uR4fxgkTAAAAlEuEJgAAAABQkqSlKaJLR9m3bJTV7c5pzn225J9zqqtLm2f0V5Xz/aYHedK1ZPFTuirx95M+Jnu91KFPyVunrnzVa2TVLyEsAQAAQDlGaAIAAAAAJUT4w+1Vcc3qPKc/svksVk1t0k7TmnQwndt/6xIN3vy2bIYvX88yIiOVMmkaV3ABAAAAuRCaAAAAAEAJkB2YnOjH6nXVvv0LclYM8eurdShBC959RrWTE066dvapEk/DJvI0bChPzC3yNL6JUyUAAADACQhNAAAAAOBsS0vLCUwsktJtdo25/VEtvqq56fCxH89Sp28/MD2RYsYXXVPOceM5VQIAAACcAqEJAAAAAJxlYWNGyiJpQ+1r9EjbZ03H3Pj3j5q56kVVdR3x6zMkyWrTkbfekf3LLyTDJ6NyFfmqVZMvKppaJQAAAEA+EZoAAAAAQHFyuxW8YJ6su3fJV6u20rr2lByOnO6j6R49UeEKbRgWbzr9lRXPqfnvnwdcPvvqLVef/vLcdqc8t91ZlLsHAAAAyhVCEwAAAAAoJiFjRilk9nRZfMeLs4eOHiFXnwFa9mB/PbPmt6zGynXyzIvdsV4vrJmhUE/6qR9iscrVd4Bco8cW5dYBAACAconQBAAAAACKitcr+7atsu7bK/ua1Qpa+V6e7oTwqur+wNP6JayOlB2YHGPzebXk7Sd1/X+/mC5tSHlqmPhCQ5VxT2s5J03Nc3IFAAAAwOkjNAEAAACAwjgWlDjWrFbQu0tlTTqY02VIMmTRrIYPamLTR0ynd7z2PPWPqaXKXTuq4n+/+IUj2XyOikr73+Py1akrX/Ua1CkBAAAAigGhCQAAAAAUhNcr+5ZNsm/ZJNsfv8u+dZNshw75Dfu16oXq2O45JYVW8uuLPrpfi5Y+rRr/66e0W26WJKUsWiw93F4V16zOM9aQ5LnhRh1ZtYaQBAAAAChmhCYAAAAAcCput4Lnz5Vj5XJV+OkHWT0e82HWChrXrIcWXdvKtH/k2nnq/vWqnJMkrt278vSnLFqslLQ0hY0ZKevOv+SrU1fO0eOk4OCi/DQAAAAAAiA0AQAAAIBA3G5FPHSfHFs2mV6ZlW3rBVeoQ/sXTPuuSvhVc1Y8r+pO/9Movlq1/ScEB8v54uTT3DAAAACAwiA0AQAAAIATeb0K69NDQSuXBwxLUhzBeuLuwfroksam/dPiJij2l42mfYYkWW1K69qzSLYLAAAAoGgQmgAAAABALo74OIUP6C1rqtO0P67+zRoYO9S0767ftmrSB1MU7k4LuL5x7FtXn/6Sw1HY7QIAAAAoQoQmAAAAAMovr1f2rZtl37IxK82w2RQyebxkGHmG7Q+trF73j9B30fVMl3l78XA1/vtH0z5DyntaxWqTq09/uUaPLZKPAAAAAKDoEJoAAAAAKJcc8XEKf3ygrMl5a41khxyGpPnXt9a4Zj1M5z/yzfsasW6+HL7MgM8wLBbJMJTWrqOM0FD5atXOupKLEyYAAABAiURoAgAAAKDcccTHKaJbJ9O+v6qcp85txyoxoppfX5XUw3rznZGqf2B3vp7ji4qWc9x4uVvFFma7AAAAAM4QQhMAAAAAZZvXK/u2rbLu2ytf9RryXH+jwkZk1STJvjYr02LV+Fu6aN4N95suMXT9QvX+YrmsMkz78zyuSlVlPNBW7hYt5WnYWLLZiuqTAAAAAChmhCYAAAAAyixHfJzCRg6VLSEhp81bpYpsSUmSpK9r1lebjuNlWKx+cy/d95deXT5W0SkHT/kcX3iE0jt0krs5QQkAAABQmhGaAAAAACgbsk+UJCbImnRQ1r//VvC82X7D0o6mavg9Q/T+pU1Nl5m0+iU9+NO6Uz7OZ7fL3fxupXfpIU/jmwhKAAAAgDKA0AQAAABAqWd2ouREay5qpN73jzDta/bnl5oSP1nnZKSe9DmGpMyLL1Hq2BflufkWghIAAACgjCE0AQAAAFD6eL2yb9kk++aNsn/xueyfbzEddjDkHPVtPVxfnn+5af/rS59W013b/doNHa93Ikm+0FBl3NNazklTJYejCD4AAAAAgJKI0AQAAABA6eH1KmTKRAXPmCqry/xUiCHpjatb6uk7+5j2t/9ujUZ/OkdBXo9pWffsNufQp+SrUzereDx1SgAAAIBygdAEAAAAQMnn9Sp4ykSFzJwqa6p5WLK7UpQeaTtGeypH+/WFpzu1eMkIXb7vrzztRni4LCkpedp8lSPlnDxN7laxRbd/AAAAAKUCoQkAAACAEs0Rt1Lhg/vJekK4IUlei1WTYzppVqO2pnMHbX5bA7cukc3w5Wk3LBb5oqJ16Mvvs6732rJRMiRPkxh5msRwqgQAAAAopwhNAAAAAJQcXq/sWzfnhBjWnX8qaNWKPPVFJOm7qIvVrv3zSrcH+S3xfwf/1oJ3x+j8I/tMH2FYslZzjhsvORzyxDSVJ6ZpUX8SAAAAAKUQoQkAAACAEiHQiZLsGiPpFRwacWdfLb/idtP5L6yZrnbff+QXsJzIFxUt57jxXL8FAAAAwA+hCQAAAICzLmTMKIXMnGoaeHxW5zp1a/OM6bwmu7/T9LgJikw7GnDt7NAlrVdfuVu0pKg7AAAAgIAITQAAAACcOV6v7Nu2yrpvr3zVa8jTsLEcq99XyMypeYYlB4VrYOwQbap9jekyr777rG7/68t8PdIXXZOTJQAAAADyhdAEAAAAQPHzehUy6UUFz5wqa3r68eaoaCnlaM4Jk3ca3KFhLQaZLnH/j2v13MezFJyZEfAxOVd5tesoz823yBcVzckSAAAAAPlGaAIAAACg+Hi9Cp4yUSEvTZA1M9Ov25qYoH8jzlW37uP1R9UL/fodmW4tWTxc1yT8lq/H+SpHyjl5GqdKAAAAAJwWQhMAAAAARevYFVyONasV9PYbfoXdJckni6Y3fkhTYjqZLtH386V6bNObqmD4Tvoon6OiMq+9Tp4bG8lz083yNInhVAkAAACA00ZoAgAAAKDIOOLjFDZyqGwJCab9P51bRx3aP6+jQWF+fRckJ2rhstGqk2w+NzdDUnrr++WcPZ+QBAAAAECRITQBAAAAUCQc8XGK6N5ZMow87Rm2Cnrm9ke1+KoWpvPGfPKKHt4en1PX5ESGlKfPW6WqnONfkju2dVFsGwAAAAByEJoAAAAAKDyvV2Ejh0qGkRNwbKp1lTo/NM50+A3//KSZK19UNdfhnLYTw5FsvtAwpfcbKG+duvJVr0FhdwAAAADFhtAEAAAAQKHZt22VLSFBRyqG6vGWj+nTi240HTdr5Qu6+7ctfu2+4BAZlc6RLTHxeFtYuNJ695Pr8WGEJAAAAADOCEITAAAAACfndit4wTxZd++Sr1ZtpXXtKTkceYZ88NdhPTUs3nR6q182avyH0xTqSffry77IK2XqLLnvuVf2bVtl3beXEyUAAAAAzgpCEwAAAAD+vF7Zt21V8Kxpcqz9RBafL6crdPQIufoM0K7HRuqJlT/r1/1OSefkmW4xfHrn7eG64d+fT/mojOYt5W59vyTJ0ySmSD8GAAAAABQEoQkAAACAPBxxKxU2bLBsSUl+fYakV667T+PD7pDmfuHX3/2rlXpy/QLZfd5TPsewWOTq3V+uMc8VxbYBAAAAoNAITQAAAADkCBkzSiEzp/oVZP+t6oXq+NA4HQyr7Dfn3DCHpj94hep9vlYRE+afdH1fcLAy610q9733K63Ho37XfAEAAADA2URoAgAAAEBS1gmTkJlTc77vtlbQ87d208LrYk3Hj1g3X+3uuUHpj/bLGt8qVkfnv6GwkUNlS0jIGeetUlUZD7SVu0VL6pQAAAAAKNEITQAAAIDy5FitEr9i616vwoc9JoukbedfrnYdXjSdfmXC75qz4jnVcGZd3eVqUC1Pv7tVrA61aElBdwAAAAClEqEJAAAAUE444uP8T4FER8s5boKcEZXUL6anPrykiencqXETde8vG/zafbVq+w+22SjoDgAAAKBUIjQBAAAAygFHfJwiuneWDCNP+4cRddT/t0pZ3zkhMLnz9881afUURbhdfusZkmS1Ka1rz+LZMAAAAACcBYQmAAAAQFnn9Sps5FDJMGSRtD+0knrfN0Lba9Y3Hf7WkhFqsuf7gMtlxy6uPv0p5A4AAACgTCE0AQAAAEq77DoliQmyJh2Ur0pV+aKic2qJ2LdtlTUhQa9dF6uxt/UyXaLz9ngN/26Fgg/sk+VUz7Pa5OrTX67RY4v8owAAAADA2URoAgAAAJRijriVCh/2mKxJB/36vNHR+unpSerzT6gShsX79Vd2HdFbS0bq0gO7JEmuXn2lebNlHDuRkpshKfPyBsp4qH3WlVycMAEAAABQBhGaAAAAAKVJ9qmSfXtlX7NaQSvf8ws4Mi1WTWz6iObc+IC0y3+JJzYuUt/Pl8mqvPVN3C1aytOwsV+xeF/Vqkp58SW5Y1sX/ecBAAAAgBKE0AQAAAAoJRzxcX6BRu7Y45ua9fRQ+xeVafP/Y369Q/9o3tLROv/Ifr8+w2LJc53XoRYtc4IZX/UaOe0AAAAAUNYRmgAAAAClgCM+ThHdO0tG3tMhafaKeuqu/lp52a2m8yZ88LLa/vipUoc+pZCjB2RYLLLkWsOwZJ1TcY4bfzwYsdnkaRJTPB8EAAAAAEowQhMAAACgpPN6FTZyqJSr1sjH/3ejej0wynR4051fa+r7k1Qp3Xl8iTp1dXT+G/5Xb0VFyzluvNytYovzEwAAAABAqUBoAgAAAJQEXq/sG9er4rIlsrhSlXljY6V17yU5HLJv2ypbQoKSgiPU794nte3CBqZLLFg2Wrfu/Ma0z1e9hjxNYrh6CwAAAABOgtAEAAAAOJu8XgVPmaiQlyfJ6nYfb/8gXqHPjFBq34FacuGNGjcs3nT6Q99/pDGfzlFQptu035Dki66ZFY5IXL0FAAAAACdBaAIAAACcSV6v7Fs3y75lo2x//C7HZ2tldTr9hu2pVEOPtBmj3WE1paS8faEZLr29ZISu3PvHSR9lSJLFkrdeCQAAAAAgIEITAAAA4AxxxMcp/PGBsiYfMu33Wqx6+aYOmt64nWn/gK1L9L/Nb8tm+PL1PF90TeqVAAAAAEABEJoAAAAAxcntVvCrc+RY9Z7s35rXG/mhxv+pXfsX5HIE+/XVSfpHC94do3Ob36agzW/79RtSTnF4SfJGRCijbQe5W95DvRIAAAAAKCBCEwAAAKCoeb2yb9uq4FnT5Pj0Y1kMw29IegWHRt3RR8sa3GG6xLiPZqrjdx/mBCKu0FAdnf+GwkYOlS0hIWecL7qm0js9Im+duhR2BwAAAIBCIjQBAAAAikp2Ufe5s2Q9fNh0yGd1rlXXNmNM+xrt+V4zVo1XlbSjfn2+WrXlbhWrQy1ayr5tq6z79hKSAAAAAEARIzQBAAAATtexEyXWfXtl2/mXgue9Ylqv5HBQmAbeM0Qb61xruszc5WN1559fmPYZkmS1Ka1rz6wGm02eJjFF9AEAAAAAALkRmgAAAAAF5fUqZMpEBc+dLevh5JzmEy/hWnrF7Rp69/9Ml2j982d6/qMZCvFkBHxM9nquPv0lh6NwewYAAAAAnBKhCQAAAFAAjvg4hT0+QLbkZL8+i6R/I6qpxwNP69dza/v1270eLXl7uK5N+DV/D7NY5Oo7UK7RYwu5awAAAABAfhCaAAAAAPnkiI9TRLdOfu0+WTSzUVtNvrmz6bxHt72rIRsXqYLhO+UzfEHByqx/qdytH1Ba916cMAEAAACAM4jQBAAAAMgPr1dhI4dKyjpRIkk7qtVWh/bP6XBwhN/wmkf2adHSp1X30H+nXjosXBkdOsvdoiWF3QEAAADgLCI0AQAAAPLBvm2rbAkJyrBV0NhmPfXmNS1Nx43+dI66fPN+TrBiJrtWScY99yr9ke5Zhd0JSgAAAADgrCM0AQAAANxuBS+YJ+vuXfLVqq20rj39rsX6cvch9RoWbzr92n93aPbKF3Ruqn+dEzNGWJhSpr0id6vYQm8dAAAAAFB0CE0AAABQfnm9Cu/TXRVXrZDFMHKaQ0ePkKvPAO1/crSe+fA3bfgrSVJlv+kzVr2oVr9uPuVjsuuUeP/v/5TRpr08MU05WQIAAAAAJRChCQAAAMoHr1f2bVtl3bdXvuo1ZElKUtigPrKlpvoNXXVJjP4Xdoc0Y6tf392/btaED6cqzJ3m12dIea7l8lWOVFrP3nINHkJIAgAAAAClAKEJAAAAyjxHfJzCRg6VLSEhp804YczesCrqdf8I/RB1sekar7RtoEbfb1TEhPEBn2NUrqzUnn3kq1NXvuo1KOoOAAAAAKUMoQkAAADKNEd8nCK6d5aME2OSLHNvuE/P39rdtK/r16s0sGkdZfbuJ0lynx+ro/Pf8AtgfJUqy9Wrj9I4UQIAAAAApVqhQpMNGzZo+fLl+u6773To0CE5HA5deOGFatq0qR5++GFFRkae1rr//vuv5s2bp82bN2vfvn0KCwvTJZdcojZt2qhVq1aF2TIAAADKsuwruBITZE06KF/lSIWNHiEZRp5rs/6ocr46PjRO+8Or+C1xbkqS3lw6Shcf/FuS5KrVS5m5+t2tYnWoRcs8V31xogQAAAAAyobTCk0yMzP15JNP6v3338/T7vF4tGPHDu3YsUNLly7VzJkzdfXVVxdo7e+//15du3ZVaq67pZOTk7Vt2zZt27ZNH330kaZMmaIKFTgkAwAAgOPMruDKzWO16YVbuuq161ub9j/52QI9+uXyPOGKJPlq1fYfbLPJ0ySmcBsGAAAAAJQ4p5U8TJ48OScwue2229SjRw/Vrl1b+/fv14YNGzRr1iwlJSWpd+/eiouLU/Xq1fO1bmJionr16qXU1FTVqlVLw4cPV4MGDXTw4EEtWrRIy5Yt08cff6zJkydr2LBhp7N1AAAAlCXHTpY41qxW8JxZpkO+PO8yte1oXofkisQ/NHfFOEWlJPn1GZJktSmta88i3DAAAAAAoCQrcGiyb98+LVq0SJJ0zz33aNKkSTl9lStX1iWXXKKGDRuqffv2Onz4sObMmaOnn346X2vPmTNHhw8fVkREhN544w2de+65kqTIyEiNGzdOISEhev311/XGG2+oQ4cOOv/88wu6fQAAAJR2breCF8yTff062b/6UtajR/yGOB3BGtZioFbXMz8NMuX9Sbpvx/qAj8iufuLq019yOIpg0wAAAACA0sBa0AmffvqpMjOzbnUePHiw6ZgGDRro1ltvlSStX78+X+seOXJEy5cvlyR17NgxJzDJbeDAgQoLC5PH49GKFSsKunUAAACUciHPjFLVC85V2Kjhqrj2E7/A5MOLG6vWsHhdPniZX2By+x9f6PuXH9Lu8a1yAhPz0vCSLBa5+g2Sa/TYov8QAAAAAIASq8AnTfbv36+goCCFhYWpZs2aAcddcMEFOePz44svvpDb7ZYkNWvWzHRMWFiYbrzxRq1du1Zr167VwIEDC7h7AAAAlDppaQodM1Ja+7GC9+zx6z4QUkl97huur8+7zHT6m0tG6KY93/u1mwUmvpBQZcS2lnPSVE6YAAAAAEA5VODQZPDgwRo8eLCcTudJx+059hfac845J1/r/vLLL5Ikq9Wq+vXrBxx36aWXau3atfrjjz/kdrvl4C+zAAAAZVb4w+1Vcc3qnOLs2d8akhZee4/G3P6o6byO336gp9fOVUVvZsC1fdE15Xz2BRlVqsi6b6981WvI07CxZLMV6WcAAAAAAJQep1UIXso69RFIYmKiNmzYIEm65ppr8rVeQkKCJKl69eqy2+0Bx0VHR0uSvF6v9u7dm3OiBQAAAKWc1yv71s2yb9koGVKFzRvk+OrLPEN2Vo7WI22f1T+VavhNPyctRW8veUqX7d9lurwhyVelqlLHviBfVDQBCQAAAADAz2mHJoH4fD6NGjVKHo9HktShQ4d8zUtOTpYkRUREnHRceHh4ztdHjvgX/QQAAEDp44iPU/jjA2VNPpSn3ZDktVg16ebOeqVhG9O5j216U/23viNr4AolWT0Wi5wTX5a7VWzRbRwAAAAAUKYUeWjy/PPPa9OmTZKkli1bqlGjRvmal5GRIUkKCgo66biKFSv6zTFjsQTsKneyfyz4MQFQXHjPACgMR3ycwrt18mvfHn2J2rV/Ue4K/qeQLz6wR/PfHaPzj+avfp6vZk2ljhsvT6tY8aoCEAh/pgFQ3HjPAChuvGcKr8hCE8Mw9Pzzz+uNN96QJF188cUaO3Zsvufbjl2NYDnFz6ZhHP8XhIHGRkaGymaz5vvZ5UWVKuGnHgQAhcB7BsBJeb3Spk1SYqIUFSXFxGS1j3oyZ0hahYoacVc/vXd5M9Mlxn84VQ/98MmpnzVokFSrllStmlSzpmwxMYrgKi4A+cSfaQAUN94zAIob75nTVyShidvt1vDhwxUfHy9Jqlu3rl577TWFhobme42QkBBJUnp6+imflS33qZPcDh1KJUnLxWLJ+k2SlJQiI/CtFQBw2njPADgVR9xKhQ19TNakgzlt3uhopXfuotB//9WndW9QjwefNp0bs2u7psVNVOX0lFM+x3vsRInfFVzJrkLtH0D5wJ9pABQ33jMAihvvmcCqVs1fkFTo0OTQoUPq16+ftm/fLkm67LLL9OqrryoyMrJA62TXKklJOflfho8ePZrzdeXKlQOO4xeEP8PgxwVA8eI9A8BMyJhRCpk51e9arMPJTg3YHaqtw+JN57227Bk12/n1Kdc3LFZl3H6n0vsOOF7cnXcRgELgzzQAihvvGQDFjffM6StUaLJ792716tVLe/bskSTFxMRo6tSpBTphkq1WrVqSpP3798vn88lqNb9eKzExUZJUoUIFnXvuuae3cQAAABQ9r1f2bVtl3bdXvuo15GnYWI7V7ytk5tScIYakxVfepaeaDzBd4sEfP9G4j2crKNNt2p+9hiR5z79Q6b16K61rT8nhKMIPAgAAAAAor047NPn111/VpUsXJScnS5Latm2r0aNHq0KF01vykksukSR5PB79/vvvqlevnum4n3/+WZL0f//3f7Lb/YuCAgAA4Aw6FpQ41qxW0LtL816/FRUtpRyVRdLf51RXlzbPaGeV8/2WCPKka/Hip3R14u+mjzAkv1MqGc1bKmXR4qL7HAAAAAAA6DRDkz179qhbt245gcmgQYPUt2/fQm3khhtuUHBwsNLS0vTZZ5+ZhiZOp1NffPGFpKxTLQAAADh7HPFxCntqiGx7E037jb17Na1JO01r0sG0v//WJRq8+W3ZDJ8MmYcj2adKMprdrooVrEo770I5R4+TgoOL6mMAAAAAAJCjwKGJ2+3W4MGDlZSUJEkaPny4unTpUuiNhISE6I477lBcXJwWLlyoe++9V9HR0XnGvPzyy0pNTZXdblenTp0K/UwAAAAUgNut4AXzZN29S5bUVAUtect02I/V66p9+xfkrBji11frUIIWvPuMaicn5Gl3DX1KwfNekSX5UJ52X+VIOSdPk+eeWFWsGq7UgynUKwEAAAAAFJsChyZLly7NuSKrefPmatOmjVJTU086J3eNk+bNm0uSGjRooAkTJuQZ99hjj+nTTz/V4cOH1bFjRz355JO6/vrrlZycrAULFmjZsmWSpM6dO6tGjRoF3ToAAABOU8joEQqZM1MWny+nLffJkHSbXaPv6K13rrzLdP7Yj2ep07cf+J0kkSRf1apyDR4i1+Ahsm/dLPuWjZIheZrEyNMkRrLZTOcBAAAAAFDUChyaLFq0KOfrNWvWaM2aNaec89tvv+V8vWvXLklStWrV/MZFRUVp2rRpGjBggBISEjRw4EC/Mc2bN9eQIUMKum0AAAAURK6i7hVfmyvHl1/4BRcWSRtqX6NH2j5rusSNf/+omateVFXXEdP+7AMjKS++JNlskiRPTFN5YpoWzWcAAAAAAKCAChSaJCcna8+ePcW1F0lZtUpWr16tuXPnavPmzdq3b58cDofq1aunBx54QPfff78sFv6tIQAAQJE7SVH33DdiHakYqkH3DNH6uteZLvPKiufU/PfP87SZ1SuRJFe/QXLHti701gEAAAAAKAoFCk0qV66c59TI6cjP/Jo1a2rMmDGFeg4AAADyzxG3UmHDBst2rG7diSySll1+m4a0HGzaH7tjvV5YM0OhnvQ87YbFIqNyZRkVK8qWeLxgvLdKVTnHv0RgAgAAAAAoUQp8PRcAAADKiGMnS4JnTZPjk49MT4L8F15NPR4YpV+q1/Hrs/m8euftJ3Xdf7+YLp9z/dakaXK3aJlz3Zeveg15GjbOuZILAAAAAICSgtAEAACgvPF6FTxlokLmzpL18GG/bp8smt3wQU1s+ojp9J5fvqehG16X3ec96WN80TXlHDde7laxkrIKuwMAAAAAUJIRmgAAAJQHueuVvP2GrCkpfkN+qVZLHds9p0Mh5/j1RR/dr0VLn9b/Jf170sdkny5Jb/2AnLNf5TQJAAAAAKBUITQBAAAoi9xuBS+YJ+vuXbKkpsqxfp1sexP9h1kraFyzHlp0bSvTZUaunafuX68yvborOyDJ02e1ytVngFyjxxb2EwAAAAAAcMYRmgAAAJQlXq/CendX0Kr38oQZxgnDtl5whTq0f8F0iasSftWcFc+ruvPQKR+XcUdzZd7cVNbdu+SrVVtpXXtKDsfp7x8AAAAAgLOI0AQAAKAs8HoVMnm8QqZMlMXrX2vEIinFEawn7h6sjy5pbLrEtLgJiv1lo2mfobwnSrwREXJOmiZ36/sLv3cAAAAAAEoIQhMAAIDSyuuVfcsmBS2cL8dHH8jq8ZgOi6t/swbGDjXta/7bFk384GWFu9NO+ihfdE2ld3pE3jp15ateQ56GjalXAgAAAAAocwhNAAAAShuvV8FTJipkxsuyulymQ/aHVlav+0fou+h6pv1vLx6uxn//eNLHGMq6fiu97wBCEgAAAABAuUBoAgAAUFpkhyUzp8qamurXbUiaf31rjWvWw3R6l6/j9NRnr8nhyzzlo3xVqyrlxZfkjm1dyE0DAAAAAFB6EJoAAACUdF6vQqZMVHCAsOTPKuepc9uxSoyo5tdXNTVZb7wzSvUP7D71Y8LCldGhs9wtWnKyBAAAAABQLhGaAAAAlGCO+DiFPT5AtuTkPO2ZFqvG39JF824wL8Q+dP1C9f5iuawyTvkMX+VIpfXsLdfgIQQlAAAAAIByjdAEAACghHLExymiW6c8bV/XrK82HcfLsFj9xl+290/Ne2+colMO+vUZkiy5vu+tUlUZD7TlVAkAAAAAALkQmgAAAJxNaWkKGzVcFbZslOwOZbRpq7RH+0s2m8JGDpUkuexBerL5AL1/aVPTJSatfkkP/rTulI/KuOFGZXTrJV/1GgQlAAAAAACYIDQBAAA4S8Ifbq+Ka1bnOQFiH/uMQsc+o/TW9+uT0AvVe9hc07nN/vxSU+In65wM/xonJzIcFXV0xhy5W5tf5QUAAAD8f3v3HR1Vtbdx/JkJM6QRSiIkAZR2RcSrvhZQESyAgiAiSAcVQUCUqjQJIhqRUC7FCwhKUbqKYAgGRcQr3Y6KiopYMBAhtFRmmDnvHyFDwpxAIDMhCd/PWq6bnF3OOVzdxnmy9w8AkI3QBAAA4CLICUzOdCi4vJ5oO0qfV79Gqus97s0VY9Tk968LdA93YKAynhqizKeHs6sEAAAAAIACIDQBAAAoapmZnsDEoux6I4v+r5Weu+cJ0+5dvlmnsR/NUaDLec6pjcAgnWh+j7Ie7S3nbbcTlgAAAAAAcB4ITQAAAIpY6LgYWST9XiFKj3Qcpz8qRnv1KZeVpmXLR6t+8p48x3eZcQcGytHsXmX1JCgBAAAAAKAwCE0AAAB8xeWSbftWWZMP5Fts3eU2NM0RqTkjEkynGLx5iQZuWS6rDEnZu1AMi0UWw/Dq6w4JVeaTA5UxZBhBCQAAAAAAPkBoAgAA4AP2hHiFxgxXQFKS55orOlppsRPlaN1Gu/YfV9+3vtWJk26pasM8Y+sc+lML3hmn6seSvebN6txN9k835pnXXaGiMvo8oUzCEgAAAAAAfIrQBAAA4EKd2lliX7dWQXNmeTU7DqbohXe+1MrdFUyHj1/3irrs/MD0+C1DkqxWpU2eLgUEnHMHCwAAAAAAKDxCEwAAgPPlcCj0mUEqu2a1rOnpXs0bat+sXg+NNR168+UVNL5VPVXv97DK7vzAtE/OQVwZTwyQ7HZJkrNRY588OgAAAAAAyB+hCQAAwNm4XLJt3Szblk8lQ7L+tkeB773rtTvkSGA5DWwzTJtq3mA6zfS60m2tm3i+T31zmfRwF5Vdt9a0f8aTg5Qx9kVfvQUAAAAAACgAQhMAAIAzuVyy/W+jQl54TmV2/yiLy5WnOXdJ9uXX3qORLQeaTtPuuw166cNZCjp5QsdfnacTZ7SnvrlMqZmZCh0zSmW2fCrZ7DrRoaMy+z7l2WECAAAAAACKDqEJAABALvaEeIU90VuWE1n59tlXvooee+g5/RJxhff4kw4tXzZKNyTtznPdXSXSfLKgIKVNnlaYRwYAAAAAAD5CaAIAAC5tp4q5W//ep7LvvCX7JxtMu7ll0Su3ddLUxt1N2/tve0tDNy1WGcOd57phscgdFZ1dvB0AAAAAABRrhCYAAOCSZU+IV2jMcAUkJeXb5/vKtdSly3ilBoZ6tV1+ZL8Wvj1WtY6Yjzcs2ZVP0mLjpIAA3zw0AAAAAADwG0ITAABwSbInxCusVw/JMLzasgJsGtesj5Zd39J07Lj1r+rhrxK8isGfyR0VrbTYODlat/HBEwMAAAAAAH8jNAEAAJcel0uhMcMlw8gTfGyqcb16dIo1HdLgr+81c/UEXZZxNN9pc+KXzD795WjZKvtILnaYAAAAAABQYhCaAACA0i2nZknyAbmrRMp5y22ybd/qOZLrWNkQDWn9tD6u08B0+KzVL+u+3VsKdCsjNFSpM15lZwkAAAAAACUUoQkAACi1zGqWuKKjdeL+tnq3/l0a2vpp03Gtf/xUcYkzFOLMMm03pDw7VAyLRVkPtFPa7NfZWQIAAAAAQAlGaAIAAEols5ol+8uFq/c9I7SrQm2p9X15+lsMt1YsHaUG+3adc253xUpy3NtSRkiI3DVqKrPn45Ld7vN3AAAAAAAARYvQBAAAlD65apZI0uyG7RV3Z0/Trr0+X62RnyyQze0665SGJFet2kqbOFXORo3ZUQIAAAAAQClEaAIAAEomk1olOUGGbftW/eqwqduTi3QotKLX0MjUQ1q0YozqpPwlKe9RW7nl7FFx3NlUx99YKgUF+eFFAAAAAABAcUFoAgAASpz8apUceXGiJoVcrRVfG1KvmV7jRn88T70/X+UJSTL69FfZhNV55smNwu4AAAAAAFxaCE0AAEDxZbKbxJ641qtWyfbq16hz1wnSz5KUNwC5LulnzVn1kiLTUrymd7RspfRxL2Xf4+99sn3+mSzJB2SUC9WJDl3kbHwHx3ABAAAAAHAJITQBAADFy6mgxJ64VmVXrlBAyumwwxUVJUvWCckwlG4P0rD7BiuxbiPTaaZuel1tt70nS65wJYdhscgdFe050svZqLEk6UTHLv55JwAAAAAAUCIQmgAAgOLB4VDoM4NUds1qWdPTTbtY9+/X2qtu11N9Rpq23/PzNk1eO1XuFW/LUvchadt7MiyWPMGJYck+nCstNo5dJAAAAAAAIA9CEwAAcNEFjx2t4Nmv5FuQ/Z+QCur34Gh9VbWeafuS5aPV6I+dnu+PJx/QiXYddHzeIq/aJ+6oaKXFxlGnBAAAAAAAeCE0AQAAF1W5h7uo7Lq1XoGJIWn+TW30YtM+puMe/jJBMR+/Lrv7pFebu0qkJMnRuo0Ot2zlVReFHSYAAAAAAMAMoQkAACgaDoeCFrwm6+975a5RU5k9H5f9/QSVXbc2T7c9larq4Y4v6O/yVbymqJRxTIuXx+jqg3tNb5GnVkmOXDVLAAAAAAAAzobQBAAA+F3wuDHZx2+53Z5rIc89K9lsskg6abFq0h2PaE7D9qbjh/3vDT2x/R1Zlas2iZRndwq1SgAAAAAAQGERmgAAAN9zuTxHYtnWva/A1Su9+xiGvrqsljp1maCTAd4/ktRL/k2vr3xRVVMP5h1mschdoaIUGKiA/dQqAQAAAAAAvkNoAgAAfMflUvCUOAXN/q+s6Wmey7l3hWTYyurZe5/S6vp3mU4x8f1p6vjdR6ZtOftM0qbMkINaJQAAAAAAwMcITQAAQOGc2lViX7dWgW/MlzUry6uLRdKHdRqqT/sxplPcuecLTUuYrApZaabtOdzRVfPsJqFWCQAAAAAA8CVCEwAAcMHsCfEKjRmugKQk0/aUoDA9+cBIbb/iWtP2hW89pzv3fnXWexhBQcp8uKccLVqxmwQAAAAAAPgVoQkAALgg9oR4hfXqIRlGnuuGpCXXt1TMvU+ajuu08wONW/+qAl3OPGMsZ/QzJDlvvkXH4hMJSgAAAAAAQJEgNAEAAAWTq7i7+7LKCh09TDIMT9jxR4VIPdphnPZWquo1NPREhpYuf1bXHvg1z3VDkixWuSMj8xZ1L19eqZOmy9G2nf/eBwAAAAAA4AyEJgAAwJzLJdvWzbJt+VQBP/8s29ZNCjh8OG8Xi1XTbu+qV27rbDrFwC1LNWjLcgUYbq+2nP0pGf0HKCPmeYq6AwAAAACAi47QBAAAeLEnxKvc0wNlPXLYtP3byDrq3OVlZdiDvNpqpfylhW8/r8uPJZ/9JtYAZTzxlDLGviiJou4AAAAAAODiIzQBAAB52BPiFfZYd6/rWWXsGtP8Cb19bXPTcePXvaIuOz/wqk0ind5VktWxi4xy5eSuUVOZPR+X7HbfPTgAAAAAAEAhEZoAAHApy12npEqknDc3VOjo4ZJOF2bfWOtG9ewwznT4rX/s1H/fi1N45vFz3irjyUGeXSUAAAAAAADFEaEJAACXmlNBiX3dWgW+85asKYc8Te7wCFlTDuloYKgG3j9Mn9a60XSKuStf1D2/7ijQ7dzlwpQ69b9ytGnri6cHAAAAAADwG0ITAAAuFQ6HQp8ZpLLxq2XNSDft8nb0/2lE70GmbQ9+/7Fe+nCmgp0nznobV6VwOW+7Xa4rr5SzURM5b7udou4AAAAAAKBEIDQBAOASEDxujIJnzZDFMLza9oVdpt7tn9NPlWt6tdlcTi1fOko3Jv1kOq9hscgdFa3UGbNlPXQw+4ivW24jJAEAAAAAACUSoQkAAKVc8LgxCp45Pc81tyyaeWtHTWnSw3RM3+3vaNinb6qM4c533pz4JS02Ts4md/roaQEAAAAAAC4eQhMAAEqTMwu733CTgme/Iim7sPuuyjXVrfNLOhoU5jW06rFkvfnWc6p9+G9Jp0OR/LijqyotNk6O1m18/BIAAAAAAAAXB6EJAAClhD0hXqExwxWQlOS55g4rL4fFqheb99XiG1qZjhv70Rw9+uUaWc64boSWkyUtNc81d7lyyuraQ44WrTiGCwAAAAAAlDqEJgAAlAL2hHiF9eoh5apZsuWK69St80um/W/c94Nmr35ZldOPeLXl1Ck5/NlO2XZsk23Lp5IhORs1lrNRY4ISAAAAAABQahGaAABQEpx57FbuXR4ul0JjhkuGoVR7sIa2HqqP/nWL6TT/fW+CWv+0Od/bGJbs/SZpsXGS3S5n4zvkbHyHz18HAAAAAACgOCI0AQCgOHO5FDR1koLnzpL16NHTl6OilPbSJDlat5Ft+1atqXClBveYazpFq582KS5xhkIdmee8nTsqmjolAAAAAADgkkVoAgBAcXNqV4l93VoFLl0ka2qqVxfr/v3KGDhIvX4P1Q8nykj3P+PVZ/nSkbrlr+893xuSV90SSXIHBirr4cfkaEmdEgAAAAAAcGkjNAEAoLhwuRQ0JU7Bs1+RNT3dtIshaW6Ddnr5rseyL5zI297zi/f07Mb5srld3oMtljw1TwyLRVkPPKi02fMISgAAAAAAAERoAgDAxedwKPSZQQp8Z4UsJ0+advk54nJ17/ii/ikX7tV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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fin_pre = x * w + b\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rc('font', size=20)\n",
    "plt.rc('figure', figsize=(20, 8), dpi=100)\n",
    "plt.scatter(x.data.numpy(), y.data.numpy(), c='r')\n",
    "plt.plot(x.data.numpy(), fin_pre.data.numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.2229)\n",
      "tensor(3.2229)\n",
      "tensor(3.0849)\n",
      "tensor(3.0849)\n",
      "tensor(2.9536)\n",
      "tensor(2.9536)\n",
      "tensor(2.8287)\n",
      "tensor(2.8287)\n",
      "tensor(2.7101)\n",
      "tensor(2.7101)\n",
      "tensor(2.5972)\n",
      "tensor(2.5972)\n",
      "tensor(2.4899)\n",
      "tensor(2.4899)\n",
      "tensor(2.3879)\n",
      "tensor(2.3879)\n",
      "tensor(2.2908)\n",
      "tensor(2.2908)\n",
      "tensor(2.1986)\n",
      "tensor(2.1986)\n",
      "tensor(2.1108)\n",
      "tensor(2.1108)\n",
      "tensor(2.0274)\n",
      "tensor(2.0274)\n",
      "tensor(1.9481)\n",
      "tensor(1.9481)\n",
      "tensor(1.8726)\n",
      "tensor(1.8726)\n",
      "tensor(1.8008)\n",
      "tensor(1.8008)\n",
      "tensor(1.7326)\n",
      "tensor(1.7326)\n",
      "tensor(1.6676)\n",
      "tensor(1.6676)\n",
      "tensor(1.6059)\n",
      "tensor(1.6059)\n",
      "tensor(1.5471)\n",
      "tensor(1.5471)\n",
      "tensor(1.4912)\n",
      "tensor(1.4912)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 1. 准备数据\n",
    "x = torch.rand([500, 1])\n",
    "y = 4 * x + 1\n",
    "\n",
    "w = torch.rand(1, requires_grad=True)\n",
    "b = torch.rand(1, requires_grad=True)\n",
    "\n",
    "for i in range(20):\n",
    "#     print(type(i))\n",
    "    # 2. 开始预测\n",
    "    y_pre = w * x + b\n",
    "\n",
    "    # 3. 计算损失\n",
    "    loss = (y - y_pre).pow(2).mean()\n",
    "    print(loss.data)\n",
    "\n",
    "    # 4. 反向传播\n",
    "    for i in [w, b]:\n",
    "        if i.grad:\n",
    "            i.grad.data.zero_()\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. 更新数据\n",
    "    w.data -= 0.01 * w.grad.data  # 仅当loss.backward()（反向传播）后w.grad才不为空，从而有w.grad.data\n",
    "    b.data -= 0.01 * b.grad.data  # 同w\n",
    "    \n",
    "    print(loss.data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3.8245], requires_grad=True)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0979], requires_grad=True)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre = x * w + b\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rc('font', size=20)\n",
    "plt.rc('figure', figsize=(20, 8), dpi=100)\n",
    "plt.scatter(x.data.numpy(), y.data.numpy(), c='r')\n",
    "plt.plot(x.data.numpy(), pre.detach().numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.rand([4, 4], requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.7969, 0.7354, 0.5071, 0.0425],\n",
       "        [0.8492, 0.4971, 0.9721, 0.3580],\n",
       "        [0.5471, 0.0430, 0.9095, 0.2030],\n",
       "        [0.7520, 0.6995, 0.0055, 0.4158]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = a * a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[6.3511e-01, 5.4077e-01, 2.5710e-01, 1.8021e-03],\n",
       "        [7.2109e-01, 2.4715e-01, 9.4507e-01, 1.2819e-01],\n",
       "        [2.9936e-01, 1.8481e-03, 8.2726e-01, 4.1229e-02],\n",
       "        [5.6543e-01, 4.8925e-01, 2.9932e-05, 1.7286e-01]],\n",
       "       grad_fn=<MulBackward0>)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Tensor.backward of tensor([[6.3511e-01, 5.4077e-01, 2.5710e-01, 1.8021e-03],\n",
       "        [7.2109e-01, 2.4715e-01, 9.4507e-01, 1.2819e-01],\n",
       "        [2.9936e-01, 1.8481e-03, 8.2726e-01, 4.1229e-02],\n",
       "        [5.6543e-01, 4.8925e-01, 2.9932e-05, 1.7286e-01]],\n",
       "       grad_fn=<MulBackward0>)>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.backward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-133-a36f8f9cb803>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'data'"
     ]
    }
   ],
   "source": [
    "a.grad.data"
   ]
  },
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   "source": []
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